{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pymongo import MongoClient\n",
    "client = MongoClient()\n",
    "db = client['tut']\n",
    "collection = db['wb']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8640\n"
     ]
    }
   ],
   "source": [
    "print(collection.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\n",
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      "3.93382352892\n",
      "3697792345300\n",
      "1.11245744204e+12\n",
      "1.7211829994e+12\n",
      "0.210598117913\n",
      "316.224430422\n",
      "1653.85377714\n",
      "725.978433646\n",
      "2.4199330423\n",
      "3257.43587635\n",
      "979.978983194\n",
      "1516.21365627\n",
      "5.04693499126\n",
      "8.33641550985\n",
      "1.36873804472e+11\n",
      "715850000000\n",
      "38.1292511572\n",
      "3.751702722e+12\n",
      "3.72264814533e+11\n",
      "3.59938813219e+11\n",
      "1882480000000\n",
      "\n",
      "4.16620491556\n",
      "3.70791766565e+12\n",
      "1.11544978267e+12\n",
      "\n",
      "330\n",
      "1658.30239124\n",
      "\n",
      "2.6489295831\n",
      "3266.3554096\n",
      "980\n",
      "\n",
      "1.38113387643e+11\n",
      "7.22333020005e+11\n",
      "38.3713516215\n",
      "38.4745646978\n",
      "965583170.475\n",
      "5050000000\n",
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      "250623555.491\n",
      "1310761200\n",
      "2696610227.27\n",
      "53910376703.1\n",
      "5.23000001907\n",
      "4.78320833333\n",
      "1.68764208774\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
      "99.075459041\n",
      "91.4546966553\n",
      "0.943059980869\n",
      "96.9769287109\n",
      "94.2814712524\n",
      "68.0720825195\n",
      "87.0249786377\n",
      "77.7850570679\n",
      "\n",
      "0.899380028248\n",
      "0.848399996758\n",
      "0.736790001392\n",
      "\n",
      "4\n",
      "26.0564098358\n",
      "21.1507091522\n",
      "20.3011207581\n",
      "21.9470405579\n",
      "\n",
      "\n",
      "\n",
      "7\n",
      "\n",
      "\n",
      "103.415382385\n",
      "\n",
      "\n",
      "\n",
      "5\n",
      "123731000\n",
      "45.8813095093\n",
      "22.3188095093\n",
      "129.702194214\n",
      "122.639663696\n",
      "136.359603882\n",
      "\n",
      "\n",
      "116.337783813\n",
      "96.8479309082\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "18.0592002869\n",
      "21.0341396332\n",
      "19.6692008972\n",
      "0\n",
      "\n",
      "\n",
      "87.3191833496\n",
      "\n",
      "\n",
      "87.3191833496\n",
      "\n",
      "\n",
      "6.51574993134\n",
      "\n",
      "\n",
      "\n",
      "5543800\n",
      "42.5718803406\n",
      "96.8479309082\n",
      "\n",
      "\n",
      "3006953\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "3.15207004547\n",
      "\n",
      "\n",
      "\n",
      "52.2525901794\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "6\n",
      "51799200\n",
      "40.9884681702\n",
      "45541200\n",
      "41.4420318604\n",
      "\n",
      "14.6069602966\n",
      "\n",
      "6258000\n",
      "37.6877593994\n",
      "37.3150405884\n",
      "31.5190505981\n",
      "42.7790298462\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "66.4625167847\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "3546200\n",
      "1077000\n",
      "30.3705406189\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "3.01247000694\n",
      "\n",
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      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "21.3\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "30.4\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "1318731\n",
      "\n",
      "1633808\n",
      "\n",
      "928027\n",
      "\n",
      "\n",
      "53.8\n",
      "51.5\n",
      "56\n",
      "29.7\n",
      "\n",
      "56.1\n",
      "97\n",
      "66.9\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "97\n",
      "98\n",
      "2.5799999237\n",
      "1.06\n",
      "0.839\n",
      "1.122\n",
      "\n",
      "\n",
      "\n",
      "25000\n",
      "360\n",
      "0.27760660907\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "47.5\n",
      "40.2\n",
      "67.8\n",
      "\n",
      "\n",
      "\n",
      "94\n",
      "\n",
      "\n",
      "\n",
      "12.1000003815\n",
      "13.1000003815\n",
      "12.6000003815\n",
      "97\n",
      "\n",
      "8.6\n",
      "2.6\n",
      "7\n",
      "\n",
      "\n",
      "5.40000009537\n",
      "5.30000019073\n",
      "5.30000019073\n",
      "33.2999992371\n",
      "31.3999996185\n",
      "32.2999992371\n",
      "\n",
      "3.79999995232\n",
      "4.69999980927\n",
      "4.19999980927\n",
      "1.39999997616\n",
      "1.39999997616\n",
      "1.39999997616\n",
      "\n",
      "21\n",
      "152\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "89.2\n",
      "66.58\n",
      "45.91\n",
      "24.37\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
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      "\n",
      "\n",
      "\n",
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      "\n",
      "\n",
      "53.4000015259\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "37.8\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "78.3000030518\n",
      "\n",
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      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "19\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
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      "\n",
      "9.89999961853\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "79\n",
      "\n",
      "77.3000030518\n",
      "\n",
      "78.1999969482\n",
      "79.0999984741\n",
      "88.9000015259\n",
      "84.1999969482\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "637334236\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "2.5\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "3.00902953248\n",
      "\n",
      "287276\n",
      "1426\n",
      "376361\n",
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      "\n",
      "\n",
      "\n",
      "\n",
      "19.8136\n",
      "\n",
      "\n",
      "115.898\n",
      "154.175\n",
      "21.06\n",
      "6.67\n",
      "\n",
      "\n",
      "39.8\n",
      "42.1\n",
      "44.3\n",
      "70.731\n",
      "69.0305121951\n",
      "67.411\n",
      "2.428\n",
      "77.32927\n",
      "71.14598\n",
      "\n",
      "\n",
      "\n",
      "\n",
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      "73.558\n",
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      "26.442\n",
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      "\n",
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      "8.30820832149\n",
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      "3657300\n",
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      "7375\n",
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      "4.238566763\n",
      "27780\n",
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      "58698.75\n",
      "\n",
      "22569000\n",
      "104.87\n",
      "107.42\n",
      "107.33\n",
      "243610\n",
      "6170.9\n",
      "9.61960863509\n",
      "29.741121334\n",
      "1.44990799265e+11\n",
      "2.64089496228e+11\n",
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      "2.15352807241e+11\n",
      "48736688986.9\n",
      "3574748479.59\n",
      "4.09080295493e+11\n",
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      "27983492260.7\n",
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      "21941883341.7\n",
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      "\n",
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      "\n",
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      "1.39837234199e+11\n",
      "2.39226146128e+11\n",
      "15.6767115745\n",
      "1.82804218155e+11\n",
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      "33503670307.7\n",
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      "16869532788.7\n",
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      "5.89151496906\n",
      "849847890000\n",
      "77.7416472134\n",
      "1946\n",
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      "35.8000759406\n",
      "\n",
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      "\n",
      "\n",
      "\n",
      "24028.7125859\n",
      "6.29488147644\n",
      "100\n",
      "100\n",
      "100\n",
      "64.9676637661\n",
      "77.4533838964\n",
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      "100\n",
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      "77.0999984741\n",
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      "195500000\n",
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      "1.40937878211e+12\n",
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      "\n",
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      "8000\n",
      "20600.6320751\n",
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      "0.997940003872\n",
      "\n",
      "\n",
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      "4\n",
      "9.4102897644\n",
      "75.5234375\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "7\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
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      "3\n",
      "7282000\n",
      "49.2035217285\n",
      "22.7562503815\n",
      "107.919960022\n",
      "107.806900024\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
      "\n",
      "7\n",
      "14273000\n",
      "\n",
      "12257000\n",
      "50.0285606384\n",
      "\n",
      "\n",
      "\n",
      "1683000\n",
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      "\n",
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      "\n",
      "\n",
      "\n",
      "\n",
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      "\n",
      "\n",
      "\n",
      "35\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "49101\n",
      "\n",
      "58899\n",
      "\n",
      "31327\n",
      "\n",
      "\n",
      "26\n",
      "22.1\n",
      "29.7\n",
      "14\n",
      "\n",
      "81.6\n",
      "97.8\n",
      "93.4\n",
      "\n",
      "\n",
      "\n",
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      "\n",
      "\n",
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      "820\n",
      "0.121949918636\n",
      "\n",
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      "\n",
      "\n",
      "\n",
      "\n",
      "72.7\n",
      "58.3\n",
      "77.9\n",
      "\n",
      "\n",
      "\n",
      "99.2\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "63\n",
      "47.41\n",
      "0.9\n",
      "1\n",
      "1\n",
      "\n",
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      "\n",
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      "\n",
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      "69\n",
      "50\n",
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      "\n",
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      "116.2\n",
      "316.078\n",
      "13.4\n",
      "11.2\n",
      "\n",
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      "18.5\n",
      "21.9\n",
      "25.2\n",
      "74.26\n",
      "68.9024390244\n",
      "63.8\n",
      "1.89\n",
      "78.6808\n",
      "51.7477\n",
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      "191\n",
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      "5.48202291244\n",
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      "100\n",
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      "100\n",
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      "5940.7506131\n",
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      "12247.5\n",
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      "75749.5\n",
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      "4.43750767559\n",
      "73869.28\n",
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      "67.6314728781\n",
      "65.3861824365\n",
      "63.2378143365\n",
      "3.27706504976\n",
      "72.000224903\n",
      "62.9910207865\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "32.9249651822\n",
      "60.9899353829\n",
      "6.08509943486\n",
      "65.5042987707\n",
      "9.97721902428\n",
      "53.9842597323\n",
      "1.73179968085\n",
      "\n",
      "\n",
      "5283057867\n",
      "49.715092143\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "3002567905\n",
      "1.09622359835\n",
      "57.073014064\n",
      "2.59411254937\n",
      "2258356126\n",
      "42.926985936\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "30.9955145284\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "3.52368151483\n",
      "9.02028801638\n",
      "10.770298448\n",
      "\n",
      "4.8596161337\n",
      "71.0846226751\n",
      "3.85448080369\n",
      "4.47339070004\n",
      "3.56686117646e+12\n",
      "78.9358164857\n",
      "13.2441404491\n",
      "5.49461439647\n",
      "\n",
      "3.23257545995\n",
      "2.15856341897\n",
      "0.805500699477\n",
      "1.78983360071\n",
      "7.08875128958\n",
      "3.27714740534e+12\n",
      "0.731291775678\n",
      "\n",
      "25.9398146423\n",
      "8.77828094788e+11\n",
      "40.6081088954\n",
      "29.0377733039\n",
      "\n",
      "\n",
      "3.10938757986\n",
      "10.7153618412\n",
      "10.894951113\n",
      "\n",
      "4.55300773372\n",
      "69.7135622108\n",
      "3.78188465028\n",
      "3.24792828217\n",
      "3.49645571823e+12\n",
      "79.3891851672\n",
      "12.0057976847\n",
      "4.25779394222\n",
      "\n",
      "2.99726862073\n",
      "2.05557686547\n",
      "1.08184684514\n",
      "1.48960527684\n",
      "7.87145826457\n",
      "3.17947259847e+12\n",
      "0.733558883549\n",
      "\n",
      "30.6192354142\n",
      "8.43113254348e+11\n",
      "\n",
      "18.1798703783\n",
      "31.3477697633\n",
      "33.6935469124\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for entry in collection.find():\n",
    "    print(entry['1990'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def indicatorByCode(country, indicator):\n",
    "    mapper = [0 for i in range(1916, 2016)]\n",
    "    entry = collection.find_one({\"Country Name\": country, \"Indicator Code\":indicator})\n",
    "    for year in range(1916, 2016):\n",
    "        if str(year) in entry.keys() and not isinstance(entry[str(year)], unicode):\n",
    "            mapper[year - 1916] = entry[str(year)]\n",
    "    return mapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "indicator_name = [\n",
    "\"AG.AGR.TRAC.NO\",\n",
    "\"AG.CON.FERT.PT.ZS\",\n",
    "\"AG.CON.FERT.ZS\",\n",
    "\"AG.LND.AGRI.K2\",\n",
    "\"AG.LND.AGRI.ZS\",\n",
    "\"AG.LND.ARBL.HA\",\n",
    "\"AG.LND.ARBL.HA.PC\",\n",
    "\"AG.LND.ARBL.ZS\",\n",
    "\"AG.LND.CREL.HA\"\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n",
      "100\n"
     ]
    }
   ],
   "source": [
    "import matplotlib\n",
    "import numpy\n",
    "%matplotlib inline\n",
    "print(len(x))\n",
    "print(len(range(1916, 2016)))\n",
    "y = {}\n",
    "for name in indicator_name:\n",
    "    y[name] = indicatorByCode(\"China\", name)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6a84c5b750>]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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K3pPjnNuMv1dNq/fHdM5NAia1sn4D/l44rW1jEXBMG2PuB+7vyFxERCrBBx/4h2H+138l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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6a84ad0150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = range(1916, 2016)\n",
    "matplotlib.pyplot.plot(x, y[indicator_name[0]])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
